期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 173, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2020.105402
关键词
Climate modeling; Deep Neural Network; Forecasting model; Greenhouse control
资金
- Korea Institute of Science and Technology [2Z05630]
- National Research Foundation of Korea [2Z05630] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Greenhouses provide controlled environmental conditions for crop cultivation but require careful management to ensure ideal growing conditions. In this study, we tested three deep-learning-based neural network models (Artificial neural network, ANN; Nonlinear autoregressive exogenous model, NARX; and Recurrent neural networks - Long short-term memory, RNN-LSTM) to determine the best approach to predicting environmental changes in temperature, humidity, and CO2 within a greenhouse to improve management strategies. This study determined the prediction performance for time steps from 5 to 30 min and showed that the accuracy of the time-based algorithm gradually decreased as prediction time increased. The best model for all datasets was RNN-LSTM, even after 30 min, with an R-2 of 0.96 for temperature, 0.80 for humidity, and 0.81 for CO2 concentration. The results of this study show that it is possible to apply deep-learning-based prediction models for more precisely managing greenhouse.
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